Geothermal area detection using Landsat ETM+ thermal infrared data and its mechanistic analysis—A case study in Tengchong, China

International Journal of Applied Earth Observation and Geoinformation 13 (2011) 552–559

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Geothermal area detection using Landsat ETM+ thermal infrared data and its mechanistic analysis—A case study in Tengchong, China Qiming Qin, Ning Zhang ∗ , Peng Nan, Leilei Chai Institute of Remote Sensing and GIS, Peking University, No. 5 Yiheyuan Road, Haidian District, Beijing 100871, China

a r t i c l e

i n f o

Article history: Received 9 July 2010 Accepted 18 February 2011 Keywords: LST Mechanism of geothermal anomaly Landsat ETM+ Geothermal detection

a b s t r a c t Thermal infrared (TIR) remote sensing is an important technique in the exploration of geothermal resources. In this study, a geothermal survey is conducted in Tengchong area of Yunnan province in China using TIR data from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor. Based on radiometric calibration, atmospheric correction and emissivity calculation, a simple but efficient single channel algorithm with acceptable precision is applied to retrieve the land surface temperature (LST) of study area. The LST anomalous areas with temperature about 4–10 K higher than background area are discovered. Four geothermal areas are identified with the discussion of geothermal mechanism and the further analysis of regional geologic structure. The research reveals that the distribution of geothermal areas is consistent with the fault development in study area. Magmatism contributes abundant thermal source to study area and the faults provide thermal channels for heat transfer from interior earth to land surface and facilitate the present of geothermal anomalies. Finally, we conclude that TIR remote sensing is a cost-effective technique to detect LST anomalies. Combining TIR remote sensing with geological analysis and the understanding of geothermal mechanism is an accurate and efficient approach to geothermal area detection. © 2011 Elsevier B.V. All rights reserved.

1. Introduction Thermal infrared remote (TIR) sensing is an efficient technique to obtain the land surface temperature (LST). With ever increasing attempts on looking for alternative energy sources, TIR remote sensing has become a popular technique in the exploration of geothermal resources. The first application of TIR remote sensing in geothermal exploration can be dated back to the middle of 20th century. In 1961, the US Army Cold Regions Research and Engineering Laboratory together with the University of Michigan conducted a geothermal survey on Yellowstone National Park in the USA with thermal infrared scanning technique and successfully detected the sign of hot springs and other near-surface geothermal anomalies (Zhou, 1998). Subsequently, Lee (1978) discovered the geothermal anomalies in Lordsbulg District of New Mexico, USA with TIR remote sensing. In the late 80s of 20th century, the thermal infrared bands of NOAA satellite were used in geothermal surveys in the east part of the Fujian Province in China. The researchers reported that the distributions of medium and low temperature areas in eastern Fujian were related to the regional deep faults caused by the intense geothermal activities in Mesozoic and Cenozoic eras (Ge, 1999). Prakash et al. (1995) proposed that thermal infrared data of

∗ Corresponding author. Tel.: +86 10 62764430; fax: +86 10 62751150. E-mail address: [email protected] (N. Zhang). 0303-2434/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.jag.2011.02.005

different times could be used to estimate the depth of buried thermal source for the same anomaly area. Yang et al. (2003) discovered the pattern of geothermal enrichment at the Tengchong district in Yunnan using Landsat Thematic Mapper (TM) thermal infrared data. Hellman and Ramsey (2004) investigated the geothermal hot springs of Yellowstone National Park using the thermal infrared data of Thermal Emission and Reflection Radiometer (ASTER) and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Vaughan et al. (2005) and Coolbaugh et al. (2007) studied the geothermal hot springs in Nevada of the United States with TIR remote sensing. Fred et al. (2008) provided a first quantitative representation of the surfacial geothermal activities in Yellowstone National Park using the ETM+ thermal infrared data. LST anomaly is a key indicator of geothermal areas in TIR remotely sensed imagery. However, it can be affected by many other factors besides geothermal resources, such as solar radiation, landforms and earthquakes. Therefore, geothermal detection using TIR remote sensing is a challenging yet interesting topic, and the mechanism of geothermal anomaly deserves further exploration to improve the accuracy of geothermal detection. Aimed at this, the paper is organized as follows. Following the introduction, the second section presents the study area and the data sets used for experiment and validation. In the third section, the methodology to retrieve LST using ETM+ thermal infrared data is stated, and the ETM+ LST result is compared with MODIS LST product for validation. The geothermal areas are identified according to the

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mechanism of geothermal anomaly which is further discussed in the fourth section. Finally, the conclusions are drawn in the fifth section. 2. Study area and data 2.1. Study area Our study area is located in E98◦ 24 –98◦ 30 , N24◦ 56 –24◦ 59 of Rehai (meaning Hot Sea) geothermal field, 11 km southwest of the Tengchong town in Yunnan province in China (Fig. 1). 2.1.1. Geothermal background of study area Tengchong area is famous for its active crustal movements, intense hydrothermal activities, and high geothermal background values (Li and Yang, 2000). It possesses the highest terrestrial heat flow value (2.82 HFU) in Yunnan province (Wu et al., 1988) with an annual average temperature at 14.8 ◦ C (Zheng and Shao, 2007). More than 70 different sizes of craters and about 140 hot spring activities have been discovered in Tengchong (Wan et al., 2005). Rehai geothermal field is one of the most typical geothermal fields in Tengchong. Therefore, it is a prospective area for geothermal resources and also a good test area for geothermal investigation using TIR remote sensing. 2.1.2. Geological setting of study area Tengchong area lies in a southern prolongation of the Himalayan high temperature geothermal belt. It is tectonically situated in the mini-Tengchong block which is on the eastern collision boundary between India and Eurasia plates (Yunnan Geology and

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Mineral Resources Bureau, 1990). In this area, quaternary volcanoes, geothermal activities and crustal earthquakes are common. The active faults with strikes of North–Northeast, North–South, North–West and North–East have been well developed in this region (Liao and Guo, 1986; Jiang et al., 1998). Our study area is shown in a rectangle of the geological map from Du et al. (2005) (Fig. 2). 2.2. Data Landsat-7 ETM+ thermal infrared data is chosen due to its high spatial resolution which benefits an accurate location of the thermal anomaly areas. The spatial resolution of ETM+ thermal band (10.45–12.5 ␮m) is 60 m × 60 m, much higher than that of the thermal infrared bands in meteorological satellite NOAA-AVHRR and MODIS sensor (generally 1 km × 1 km). The data were obtained on January 13, 2002 considering the less heating influence from solar radiation on land surface in winter. The image is in good quality with 3% cloud coverage. Validation of the ETM+ LST is conducted by comparing it with the MODIS LST product. In this work, version 5 of MODIS/Terra Land Surface Temperature and Emissivity (LST/E)Daily L3 Global 1 km Grid products (MOD11A1) are downloaded from https://wist.echo.nasa.gov/api/ and used. 3. Land surface temperature retrieval 3.1. The physical basis of LST retrieval In accordance with blackbody theory, the emitted radiance from an object can be calculated from Planck’s radiance function: B(, T ) =

c1 −5 (exp(c2 /T ) − 1)

(1)

where B(, T) is the spectral radiance of the blackbody in units of W m−2 ␮m−1 sr−1 , and in practice, it is the emitted radiance of ground object.  is wavelength in meters, T is temperature in K, c1 and c2 are the spectral constants with c1 = 3.7418 × 10−16 W m2 and c2 = 1.4388 × 10−2 m K. When the emitted radiance of ground object B(, T) is measured, generally by thermal sensor, the temperature T can be computed by inverting the Planck’s radiance function as follows: T=

c2  ln[(c1 /5 B(, T )) + 1]

(2)

In fact, T is the “brightness temperature”, and more developed algorithm as well as further data preprocessing, such as radiometric calibration and atmospheric correction, are required to achieve the real surface temperature. The most robust algorithm for retrieving real LST is the Split Window algorithm. However, this method is only applicable for the sensor with two or more thermal channels. For the sensor with one thermal channel, the ETM+ data in our case, a single-channel algorithm is preferred. 3.2. Methodology of LST retrieval

Fig. 1. Location of study area in Tengchong, Yunnan province, China (study area is marked by red rectangle). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

The main steps for LST retrieval in this study include radiometric calibration, atmospheric correction and emissivity calculation. The specific process is shown in Fig. 3. To begin with, radiometric calibration is applied to convert the Digital Number (DN) recorded by the remote sensor into the at-atmosphere radiance. In this paper, radiometric calibration is carried out according to the Landsat 7 Science Data Users Handbook (2009).

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Fig. 2. Geological map of Tengchong area in Yunnan province, China (Du et al., 2005). Our study area is showed in a rectangle and the location of Rehai area and Tengchong city are marked by black dots.

3.2.1. Atmospheric correction The at-atmosphere radiance is largely contaminated by the atmospheric conditions. Thus, atmospheric correction is required to retrieve the real surface parameters by removing the atmospheric effects, such as the absorption, upward emission and scattering of the radiation from the earth surface. 6S model is a popular atmospheric correction model based on the simulation of the radiative transfer process to derive the at-surface reflectance with accredited precision. Therefore, we apply 6S model to remove the atmospheric effect from the ETM+ data of the third band (red band) and fourth band (near infrared band). Correction results are listed in Table 1.

Table 1 Results of 6S atmospheric correction in visual and near-infrared bands. Band

Coefficient of xa

Coefficient of xb

Coefficient of xc

ETM+ Band 3 ETM+ Band 4

0.0041747 0.0055641

0.0381220 0.0181545

0.0765704 0.0485249

y = xa × (measured radiance) − xb ; arc = y/(1 + xc × y); measured radiance is the top of atmosphere radiance; arc is the albedo of surface.

3.2.2. Emissivity calculation Emissivity is a key variable in LST retrieval. There are two ways to obtain the emissivity: firstly, look-up-table (LUT) method, in which the emissivity values are assigned to each class derived from conventional image classification; secondly, pixel level emissivity is calculated using vegetation fraction from Normal Differential Vegetation Index (NDVI). The accuracy of the LUT method is greatly hindered by mixed pixel issues and depends on the precision of classification. Therefore, the second approach proposed by Sobrino et al. (2001) is applied to calculate emissivity in this study. The following equation is used to compute NDVI from the atmosphere corrected red reflectance (Rred ) and near infrared reflectance (Rnir ) from ETM+ data: NDVI =

Rnir − Rred Rnir + Rred

(3)

Vegetation fraction is the percentage of the vertical projection of vegetation canopy (including leaves, stems, and branches) in per unit area. It can be derived from NDVI according to Eq. (4) (Carlson and Ripley, 1997):

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3.2.3. LST calculation A single-channel algorithm proposed by Artis and Carnahan (1982) is adopted in this paper, since it requires the least amount of parameters for temperature retrieval compared to the mono-window algorithm (Qin et al., 2001) and the generalized ˜ single-channel method (Jiménez-Munoz and Sobrino, 2003). The formula is as follows: Ts =

Tsensor 1 + ( × Tsensor /) ln ε

(6)

 is the wavelength of emitted radiance (the average wavelengths  = 11.5 ␮m (Weng et al., 2004) is used).  = h × c/j in m K, where h is Planck’s constant (6.626 × 10−34 J s), c is the velocity of light (2.998 × 108 m/s), and j is Boltzmann constant (1.38 × 10−23 J/K). Tsensor is at-sensor brightness temperature in K given by Tsensor =

K2 ln(1 + K1 /Lsensor )

(7)

with K2 = 1282.7108 K, K1 = 666.093 W m−2 sr−1 ␮m−1 . 3.3. LST results and validation

Fig. 3. Flow Chart of LST Retrieval.

Pv =

 NDVI − NDVI 2 s NDVIv − NDVIs

(4)

where NDVIv and NDVIs represents the NDVI of vegetation and soil, respectively. We approximately take NDVIv as 0.65 and NDVIs as 0.2 according to the NDVI histogram values of our study area. The NDVI Thresholds Method—NDVITHM proposed by Sobrino et al. (2001) is a modification of a semi-empirical method to compute emissivity with acceptable performance. Considering the landform of our study area, the emissivity values are calculated with different cases: (a) NDVI < 0.2 In this case, the pixel is considered as bare soil (Pv = 0) and the soil emissivity (εs ) is assumed as a mean value of 0.97 (Sobrino et al., 2004, 2008). (b) NDVI > 0.65 Pixels with NDVI values higher than 0.65 are considered fully vegetated (Pv = 1) with a constant value of vegetation emissivity (εv ) of 0.99 (Sobrino et al., 2004, 2008). (c) 0.2 ≤ NDVI ≤ 0.65 In this case, the pixel is composed by a mixture of bare soil and vegetation, and the emissivity is calculated according to the following equation (Sobrino et al., 2004): ε = mPv + n

(5)

with m = εv − εs − (1 − εs )Fεv , n = εs + (1 − εs )Fεv , where F is a shape factor (Sobrino et al., 1990) whose mean value, assuming different geometrical distributions, is 0.55. Moreover, the emissivity data (30 m) from visible and nearinfrared bands are resampled to the same spatial resolution as thermal infrared band (60 m) for the next LST calculation.

The final results are shown in the LST map (Fig. 4). The map shows that the lowest temperature in study area is 281.30 K and the highest is 295.84 K with color ranges from purple to red. Four areas with conspicuous red color are picked out and marked with A, B, C and D. The statistics reveal that the temperature of A, B, C, D areas are overall 4–10 K higher than the background temperature. In order to see if the retrieved ETM+ temperature is valid, we compare the ETM+ LST with MODIS/Terra LST product on January 13, 2002. The original ETM+ LST result (60 m) is resampled to 1 km to match the spatial resolution of MODIS LST product. The results are shown in Table 2. The comparison between 60-m ETM+ LST and 1-km ETM+ LST (Table 2) demonstrates a decrease in the average temperature and a narrow down in the temperature ranges for A, B, C and D areas of the 1-km ETM+ LST. Our explanation to this is the average effect during the resample process. Due to the heterogeneity of thermal anomaly areas (standard deviation from 1.4 K to 1.82 K), the highest temperature is averaged with the surrounding lower temperature, which in the end yields a narrower temperature range than the original LST range. The comparison between 1-km ETM+ LST and 1-km MODIS LST shows that the average temperature differences are all within 1.3 K (Table 2) for A, B, C and D areas. The result is consistent with the Srivastava’s discovery (Srivastava et al., 2009) that LST comparison between MODIS and ETM+ has a maximum difference of 2 ◦ C. On this account, the accuracy of retrieved ETM+ LST in this study is acceptable for geothermal detection. Therefore, we consider the four LST anomalous areas (A, B, C and D) as the potential geothermal areas where the mechanism of geothermal anomaly is required for identification.

4. Mechanistic analysis of geothermal anomaly 4.1. Mechanism of geothermal anomaly Land surface temperature is mainly generated from solar radiation which accounts for a throughout heating of land surface and the Earth’s interior heat which is responsible for a localized temperature increase. The understanding of surface energy balance and underground heat transfer will contribute to the identification of geothermal areas caused by Earth’s interior heat.

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Fig. 4. LST map of study area with the lowest temperature at 281.30 K and the highest temperature at 295.84 K. Four thermal anomalous areas are marked with A, B, C, and D.

At land surface, temperature is the result of balanced surface energy radiation (Zhang, 1999). In general, the surface energy balance can be simply expressed according to Monteith (1973): Qd = H + E + G

(8)

where Qd is the net radiation received by land surface; H denotes the sensible heat flux between the land surface and the lower atmosphere; E stands for the latent heat flux in the phase transition of water between the underlying surface and the atmosphere; G is the soil heat flux characterizing the thermal exchanges among different depths of soil. For local area the sensible heat flux H and latent heat flux E can be assumed unchanged, then soil heat flux (G) is a major factor affecting the balance of surface energy (Guo and Sun, 2002). In the lithosphere, the heat is mainly transferred in thermal conduction (Rudnick et al., 1998; Zang et al., 2002). This process can be described by the following equation:

∇ (K ∇ T ) = −A

(9)

where T stands for temperature, K denotes the rock thermal conductivity, and A is the radiogenic heat production of rocks. For local area, heat flow is dominated by rock thermal conductivity K, which is mainly affected by rock’s physical and chemical properties (Li, 1992; Ou et al., 2004; Xiong et al., 1994), such as the mineral components, the rock porosity and the fracture filler. Thermal convection

is another important form of underground heat transfer. It exists in the upwelling of underground thermal materials, such as hot water, heated gases, even the up-surging magma along the rock cracks. In conclusion, the inner heat is transferred to land surface through thermal conduction and convection, during which process the changes of soil heat flux break the balance of surface energy and cause geothermal anomalies. Therefore, underground heat source and available thermal channels are two critical factors to determine geothermal areas. These two factors are further examined for study area in the following text. 4.2. Analysis of geothermal anomaly in study area 4.2.1. Heat source analysis The Chinese researchers have applied a variety of geophysical and geochemical techniques for geothermal detection in Tengchong area since 1970s, especially in Rehai geothermal field. Recent efforts are listed as below. The Magnetotelluric (MT) sounding surveys (Bai et al., 1994, 2001) showed that a conductive core zone existed under Tengchong Rehai area, which was suggested to be a magma chamber. The experiments of seismic velocity tomography (Lou et al., 2002; Wang et al., 2002; He et al., 2004; Wang and Huangfu, 2004) also indicated the existence of a magma chamber below the Rehai

Table 2 Statistical comparison between ETM+ LST and MODIS LST on January 13, 2002. Location

A

Sensor ETM+ MODIS

B

ETM+ MODIS

C

ETM+ MODIS

D

ETM+ MODIS

Spatial resolution

Minimum (K)

Maximum (K)

Average (K)

Standard deviation (K)

60 m 1 km 1 km 60 m 1 km 1 km 60 m 1 km 1 km 60 m 1 km 1 km

285.68 287.80 289.22 286.50 287.80 289.62 286.83 289.46 290.36 285.08 287.64 288.47

295.15 290.55 291.17 295.84 289.51 290.29 295.45 289.64 291.04 295.61 290.35 290.79

290.81 288.89 289.97 290.95 288.65 289.95 291.11 289.55 290.69 290.54 288.71 289.83

1.65 1.17 0.93 1.82 1.20 0.48 1.41 0.13 0.48 1.70 1.15 0.93

AverageMODIS − AverageETM+

1.08

1.30

1.14

1.12

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Fig. 5. Comparison between topographic map and the LST map. (a) Three-dimensional topographic map of study area marked with four thermal anomalous areas; (b) LST map of study area.

geothermal field. The evidence from helium/carbon isotope experiments (Dai, 1988; Wang et al., 1993; Xu et al., 1994; Shangguan et al., 2000) revealed that the emitting gases from the discharges of high geothermal gradient regions at Rehai area were related to the intruding mantle-derived magma nearby. Zhao (2008) summarized the previous evidence and identified three magma chambers using the geochemical temperature scale and the helium/carbon isotope techniques, and the most active magma chamber was found to be located under the zone of Tengchong City, Rehai area and Qingshui village. On the summary of previous researches, it can be inferred that the intrusive activities of mantle-derived magma exist in the shallow crust of Rehai geothermal field and are accompanied with the intensive mantle gas emissions. The magmatism serves as a powerful heat source for modern geothermal activities in study area. 4.2.2. Geological structure analysis In our case, the tectonic movements along with the intruding materials altered the chemical and physical properties of rocks in Tengchong area and directly impacted on the rock thermal conductivity. According to the mechanism of thermal conduction, thermal conductivity characterizes the transfer speed of thermal energy. Taking account of the underground thermal source, the higher the thermal conductivity is, the faster the heat is transferred to the upper layer of Earth. The regional geology of the Yunnan province (Yunnan Geology and Mineral Resources Bureau, 1990) recorded that the strata of the Tengchong block above the Cambrian were all in piecesdistribution, and the quaternary volcanic rocks of Rehai Geothermal Field primarily consist of quaternary sediments and granite. Referring to the thermal conductivities of the common materials listed in Table 3 (Li, 2008), the thermal conductivity of air (0.161) and granite (0.019) are much higher than that of soil (0.007) and water (0.0015). Thus, the chemical and physical properties of rocks in study area benefit a prompt upward thermal transmission from underground heat. Further geological investigations reveal a close correlation between the faulted structure and the distribution of geothermal anomalies. With the knowledge of study area in Section 2, we can find that most of the volcanoes (Jiang et al., 1998; Du et al., 2005)

Table 3 Thermal conductivities of common materials (Li, 2008). Materials

K (cm2 /s)

Soil Granite Water Air

0.0070 0.0190 0.0015 0.161

and hot springs (Shangguan et al., 2004) are in string-like distribution along the faults running from North to North–West and North–East (Fig. 2). Accordingly, the geochemical characteristics of geothermal fluids also indicate that the geothermal reservoir in our study area is of multi-layer structure and subjected to the development of active faults (Zhao et al., 1995; Shangguan, 2000). Helium isotope experiments (Shangguan et al., 2000) demonstrate that the deep-level and mid-level geothermal reservoirs and their fluid release activities are directly impacted by the North–South and North–West faults, respectively. In order to have a direct impression on the tectonic landform of study area, we achieve a three-dimensional (3-D) topographic map (Fig. 5(a)) with the topography simulation function of Google Earth software. Four LST anomaly areas (A, B, C and D) are marked by their center points in Fig. 5(a). A simplified LST map (Fig. 5(b)) is listed aside for comparison. Fig. 5(a) demonstrates that A to D are all located in the fault valley with an inclined “V” distribution, which is consistent with the “V”-shaped line connecting the thermal anomalous areas in Fig. 5(b). It can be well explained that the faulted structure provides passageways for thermal transmission with which the underground heat can be efficiently transferred to land surface and detected as LST anomalies in thermal infrared images. 4.2.3. Identification of geothermal areas Based on the analysis of heat source and geological structure, we conclude that the underground magmatism contributes abundant thermal source for Tengchong area, and the faulted structure in study area serves as thermal channel for heat transfer. With the two available prerequisites, underground heat is transferred to land surface and disturbs the surface energy balance, which takes on surface thermal anomalies. Therefore, the LST anomalies (A, B, C and D) in this research can be determined as geothermal areas. The validation of selected geothermal areas is carried out by field investigation in 2008. The result showed that Rehai Wonders (geothermal area A), Rehai Volcanic Area (geothermal areas B and C) and Rehai Hot Spring (geothermal area D) are three famous geothermal landscapes in study area and have developed into the tourist attractions integrating sightseeing, spa bath and medical care. Boiled and hot springs, geysers, hydrothermal explosions and high-temperature fumaroles are widely distributed in the four geothermal areas. 5. Conclusions In this paper, a geothermal detection is carried out in Tengchong, Yunnan province of China. Surface temperature is retrieved from ETM+ TIR data and four geothermal areas are determined with the geothermal mechanistic analysis and regional geologic investiga-

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tion. The results demonstrate that the distribution of geothermal areas correlates closely with the development of faulted structure. The magmatism in study area serves as heat source for geothermal anomalies and the faults provide the thermal channels for heat transfer which disturbs the localized balance of surface energy and cause thermal anomalies at land surface. The results of this work also suggest that TIR remote sensing is an important technique for geothermal exploration with its high efficiency, simplicity and accuracy in temperature retrieval. However, it can only detect the surfacial thermal anomaly and is sensitive to the shallow buried geothermal resources. Therefore, geologic analysis and the mechanism of geothermal anomaly are required to assist and facilitate the identification of geothermal areas with TIR remote sensing. With the knowledge of geothermal mechanism and the complement from the geological investigation, the accuracy of geothermal detection using TIR remote sensing can be much improved. Acknowledgements The authors appreciate the kind financial support of the High-Tech Research and Development Program of China (2009AA12Z128) and the National Natural Science Foundation of China (40771148 and 41071221). References Artis, D.A., Carnahan, W.H., 1982. Survey of emissivity variability in thermography of urban areas. Remote Sensing of Environment 12 (4), 313–329. Bai, D., Liao, Z., Zhao, G., Wang, X., 1994. Deducing the magma resources by the result of MT sounding in Rehai geothermal field in Tengchong. China Science Bulletin 39 (4), 344–347 (in Chinese). Bai, D., Meju, M.A., Liao, Z., 2001. Magnetotelluric images of deep crustal structure of the Rehai geothermal field near Tengchong, southern China. Geophysical Journal International 147 (3), 677–687. Carlson, T.N., Ripley, D.A., 1997. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment 62 (3), 241–252. Coolbaugh, M.F., Kratt, C., Fallacaro, A., Calvin, W.M., Taranik, J.V., 2007. Detection of geothermal anomalies using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) thermal infrared images at Bradys Hot Springs, Nevada, USA. Remote Sensing of Environment 106 (3), 350–359. Dai, J.X., 1988. The characteristics and causes of carbon isotope composition of the natural gas in sulfur ponds of Tengchong, Yunnan. Chinese Science Bulletin 33 (15), 1168–1170 (in Chinese). Du, J., Liu, C., Fu, B., Ninomiya, Y., Zhang, Y., Wang, C., Wang, H., Sun, Z., 2005. Variations of geothermometry and chemical-isotopic compositions of hot spring fluids in the Rehai geothermal field, southwestern China. Journal of Volcanology and Geothermal Research 142, 243–261. Fred, G.R, Watson, R.E., Lockwood, W.B., Newman, T.N., Anderson, R.A.G., 2008. Development and comparison of Landsat radiometric and snowpack model inversion techniques for estimating geothermal heat flux. Remote Sensing of Environment 112 (2), 471–481. Ge, B., 1999. The application of remote sensing through twenty years from aerial colour infrared photograph interpretation to aerospace thermal infrared data analysis. Progress in Geophysics 14 (3), 138–140 (in Chinese with English abstract). Guo, W., Sun, S., 2002. Preliminary study on the effects of soil thermal anomaly on land surface energy budget. Acta Meteorologica Sinica 60 (6), 706–714 (in Chinese with English abstract). He, C., Wang, C., Wu, J., 2004. S-wave velocity structure inferred from receiver function inversion in Tengchong volcanic area. Acta Seismologica Sinica 26 (1), 11–18 (in Chinese with English abstract). Hellman, M.J., Ramsey, M.S., 2004. Analysis of hot springs and associated deposits in Yellowstone National Park using ASTER and AVIRIS remote sensing. Journal of Volcanology and Geothermal Research 135 (1–2), 195–219. Jiang, C., Zhou, R., Yao, X., 1998. Fault structure of Tengchong volcano. Journal of Seismological Research 21 (4), 330–336 (in Chinese with English abstract). ˜ J.C., Sobrino, J.A., 2003. A generalized single channel method for Jiménez-Munoz, retrieving land surface temperature from remote sensing data. Journal of Geophysical Research 108 (D22), 4688, doi:10.1029/2003JD003480. 7 Science Data Users Handbook [M/OL], 2009. Landsat http://landsathandbook.gsfc.nasa.gov/handbook/handbook toc.html. Lee, K., 1978. Analysis of thermal infrared imagery of the Black Rock Desert geothermal area. Colorado School of Mines Quarterly 4 (2), 31–44.

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